code: def test_attach_model_callbacks():
      """Test that the callbacks defined in the model and through Trainer get merged correctly."""
  
      def _attach_callbacks(trainer_callbacks, model_callbacks):
          model = LightningModule()
          model.configure_callbacks = lambda: model_callbacks
          has_progress_bar = any(
              isinstance(cb, ProgressBar)
              for cb in trainer_callbacks + model_callbacks
          )
          trainer = Trainer(
              enable_checkpointing=False,
              enable_progress_bar=has_progress_bar,
              enable_model_summary=False,
              callbacks=trainer_callbacks,
          )
          trainer.strategy._lightning_module = model
          cb_connector = _CallbackConnector(trainer)
          cb_connector._attach_model_callbacks()
          return trainer
  
      early_stopping1 = EarlyStopping(monitor="red")
      early_stopping2 = EarlyStopping(monitor="blue")
      progress_bar = TQDMProgressBar()
      lr_monitor = LearningRateMonitor()
      grad_accumulation = GradientAccumulationScheduler({1: 1})
  
      # no callbacks
      trainer = _attach_callbacks(
          trainer_callbacks=[], model_callbacks=[]
      )
      assert trainer.callbacks == []
  
      # callbacks of different types
      trainer = _attach_callbacks(
          trainer_callbacks=[early_stopping1],
          model_callbacks=[progress_bar],
      )
      assert trainer.callbacks == [early_stopping1, progress_bar]
  
      # same callback type twice, different instance
      trainer = _attach_callbacks(
          trainer_callbacks=[
              progress_bar,
              EarlyStopping(monitor="red"),
          ],
          model_callbacks=[early_stopping1],
      )
      assert trainer.callbacks == [progress_bar, early_stopping1]
  
      # multiple callbacks of the same type in trainer
      trainer = _attach_callbacks(
          trainer_callbacks=[
              LearningRateMonitor(),
              EarlyStopping(monitor="yellow"),
              LearningRateMonitor(),
              EarlyStopping(monitor="black"),
          ],
          model_callbacks=[early_stopping1, lr_monitor],
      )
      assert trainer.callbacks == [early_stopping1, lr_monitor]
  
      # multiple callbacks of the same type, in both trainer and model
      trainer = _attach_callbacks(
          trainer_callbacks=[
              LearningRateMonitor(),
              progress_bar,
              EarlyStopping(monitor="yellow"),
              LearningRateMonitor(),
              EarlyStopping(monitor="black"),
          ],
          model_callbacks=[
              early_stopping1,
              lr_monitor,
              grad_accumulation,
              early_stopping2,
          ],
      )
      assert trainer.callbacks == [
          progress_bar,
          early_stopping1,
          lr_monitor,
          grad_accumulation,
          early_stopping2,
      ]
  
      class CustomProgressBar(TQDMProgressBar):
          ...
  
      custom_progress_bar = CustomProgressBar()
      # a custom callback that overrides ours
      trainer = _attach_callbacks(
          trainer_callbacks=[progress_bar],
          model_callbacks=[custom_progress_bar],
      )
      assert trainer.callbacks == [custom_progress_bar]
  
      # edge case
      bare_callback = Callback()
      trainer = _attach_callbacks(
          trainer_callbacks=[bare_callback],
          model_callbacks=[custom_progress_bar],
      )
      assert trainer.callbacks == [bare_callback, custom_progress_bar]
  
imports: import contextlib
  import logging
  from unittest import mock
  from unittest.mock import Mock
  import pytest
  import torch
  from lightning.fabric.utilities.imports import _PYTHON_GREATER_EQUAL_3_8_0, _PYTHON_GREATER_EQUAL_3_10_0
  from lightning.pytorch import Callback, LightningModule, Trainer
  from lightning.pytorch.callbacks import EarlyStopping, GradientAccumulationScheduler, LearningRateMonitor, ModelCheckpoint, ModelSummary, ProgressBar, TQDMProgressBar
  from lightning.pytorch.callbacks.batch_size_finder import BatchSizeFinder
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.trainer.connectors.callback_connector import _CallbackConnector
label_desc: Use image node_id 3 for calling a global function with example usage: test_attach_model_callbacks() and returns: trainer